ROBP

A Python implementation of " ROBP: A robust border-peeling clustering using Cauchy kernel"

introduction

A Python implementation of the clustering algorithm presented in:

Mingjing Du, Ru Wang, Ru Ji, Xia Wang, Yongquan Dong. ROBP: A robust border-peeling clustering using Cauchy kernel. Information Sciences, 2021, 571: 375-400.

The paper is available online at: pdf.

If you use this implementation in your work, please add a reference/citation to the paper. You can use the following bibtex entry:

@article{du2021robp,
  title={ROBP: a robust border-peeling clustering using Cauchy kernel},
  author={Du, Mingjing and Wang, Ru and Ji, Ru and Wang, Xia and Dong, Yongquan},
  journal={Information Sciences},
  volume={571},
  pages={375--400},
  year={2021},
  doi={10.1016/j.ins.2021.04.089}
}

ROBP is an improved version of Border-Peeling Clustering (TPAMI,2020)

The Python implementation of original paper (BP clustering) can be found here .

Requirement

• Python >= 3.6
• numpy
• scipy
• scikit-learn
• python_algorithms

Installation instructions

In our implementation we use the numpy, scipy and scikit-learn packages. We recommend using the Anaconda python distribution which installs these packages automatically.

Please note that the current code only supports python 3.6.

For the UnionFind implementation We also use the python_algorithms package: https://pypi.python.org/pypi/python_algorithms

If you are using the pip package manager, you can install it by running the command:

pip install python_algorithms

Usage instructions

In order to run ROBP clustering, run the file run_robp.py using python.

The following command line arguments are available for run_robp.py:

usage: run_robp.py [-h] --input <file path> --output <file path> [--no-labels]
                 [--pca <dimension>] [--spectral <dimension>]


optional arguments:
  -h, --help            show this help message and exit
  --input <file path>   Path to comma separated input file
  --output <file path>  Path to output file
  --no-labels           Specify that input file has no ground truth labels
  --pca <dimension>     Perform dimensionality reduction using PCA to the
                        given dimension before running the clustering
  --spectral <dimension>
                        Perform spectral embedding to the given dimension
                        before running the clustering (If combined with PCA,
                        PCA is performed first)